feat: chat on mps

examples/document_search.py

@@ -74,7 +74,7 @@ def main():
     print(f"⏱️  Basic search time: {basic_time:.3f} seconds")
     print(">>> Basic search results <<<")
     for i, res in enumerate(results, 1):
-        print(f"  {i}. ID: {res['id']}, Score: {res['score']:.4f}, Text: '{res['text']}', Metadata: {res['metadata']}")
+        print(f"  {i}. ID: {res.id}, Score: {res.score:.4f}, Text: '{res.text}', Metadata: {res.metadata}")
 
     # --- 3. Recompute search demo ---
     print(f"\n[PHASE 3] Recompute search using embedding server...")
@@ -107,7 +107,7 @@ def main():
         print(f"⏱️  Recompute search time: {recompute_time:.3f} seconds")
         print(">>> Recompute search results <<<")
         for i, res in enumerate(recompute_results, 1):
-            print(f"  {i}. ID: {res['id']}, Score: {res['score']:.4f}, Text: '{res['text']}', Metadata: {res['metadata']}")
+            print(f"  {i}. ID: {res.id}, Score: {res.score:.4f}, Text: '{res.text}', Metadata: {res.metadata}")
         
         # Compare results
         print(f"\n--- Result comparison ---")
@@ -116,8 +116,8 @@ def main():
         
         print("\nBasic search vs Recompute results:")
         for i in range(min(len(results), len(recompute_results))):
-            basic_score = results[i]['score']
+            basic_score = results[i].score
-            recompute_score = recompute_results[i]['score']
+            recompute_score = recompute_results[i].score
             score_diff = abs(basic_score - recompute_score)
             print(f"  Position {i+1}: PQ={basic_score:.4f}, Recompute={recompute_score:.4f}, Difference={score_diff:.4f}")
         

examples/main_cli_example.py

@@ -1,6 +1,7 @@
 import faulthandler
 faulthandler.enable()
 
+import argparse
 from llama_index.core import SimpleDirectoryReader, Settings
 from llama_index.core.readers.base import BaseReader
 from llama_index.node_parser.docling import DoclingNodeParser
@@ -69,17 +70,30 @@ if not INDEX_DIR.exists():
 else:
     print(f"--- Using existing index at {INDEX_DIR} ---")
 
-async def main():
+async def main(args):
     print(f"\n[PHASE 2] Starting Leann chat session...")
-    chat = LeannChat(index_path=INDEX_PATH)
+    
+    llm_config = {
+        "type": args.llm,
+        "model": args.model,
+        "host": args.host
+    }
+
+    chat = LeannChat(index_path=INDEX_PATH, llm_config=llm_config)
     
     query = "Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?"
     query = "What is the main idea of RL and give me 5 exapmle of classic RL algorithms?"
     query = "什么是盘古大模型以及盘古开发过程中遇到了什么阴暗面，任务令一般在什么城市颁发"
 
     print(f"You: {query}")
-    chat_response = chat.ask(query, top_k=20, recompute_beighbor_embeddings=True,complexity=32,beam_width=1)
+    chat_response = chat.ask(query, top_k=20, recompute_beighbor_embeddings=True, complexity=32)
     print(f"Leann: {chat_response}")
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    parser = argparse.ArgumentParser(description="Run Leann Chat with various LLM backends.")
+    parser.add_argument("--llm", type=str, default="hf", choices=["simulated", "ollama", "hf", "openai"], help="The LLM backend to use.")
+    parser.add_argument("--model", type=str, default='meta-llama/Llama-3.2-3B-Instruct', help="The model name to use (e.g., 'llama3:8b' for ollama, 'deepseek-ai/deepseek-llm-7b-chat' for hf, 'gpt-4o' for openai).")
+    parser.add_argument("--host", type=str, default="http://localhost:11434", help="The host for the Ollama API.")
+    args = parser.parse_args()
+
+    asyncio.run(main(args))

examples/multi_vector_aggregator.py

@@ -0,0 +1,319 @@
+#!/usr/bin/env python3
+"""
+Multi-Vector Aggregator for Fat Embeddings
+==========================================
+
+This module implements aggregation strategies for multi-vector embeddings,
+similar to ColPali's approach where multiple patch vectors represent a single document.
+
+Key features:
+- MaxSim aggregation (take maximum similarity across patches)
+- Voting-based aggregation (count patch matches)
+- Weighted aggregation (attention-score weighted)
+- Spatial clustering of matching patches
+- Document-level result consolidation
+"""
+
+import numpy as np
+from typing import List, Dict, Any, Tuple, Optional
+from dataclasses import dataclass
+from collections import defaultdict
+import json
+
+@dataclass
+class PatchResult:
+    """Represents a single patch search result."""
+    patch_id: int
+    image_name: str
+    image_path: str
+    coordinates: Tuple[int, int, int, int]  # (x1, y1, x2, y2)
+    score: float
+    attention_score: float
+    scale: float
+    metadata: Dict[str, Any]
+
+@dataclass
+class AggregatedResult:
+    """Represents an aggregated document-level result."""
+    image_name: str
+    image_path: str
+    doc_score: float
+    patch_count: int
+    best_patch: PatchResult
+    all_patches: List[PatchResult]
+    aggregation_method: str
+    spatial_clusters: Optional[List[List[PatchResult]]] = None
+
+class MultiVectorAggregator:
+    """
+    Aggregates multiple patch-level results into document-level results.
+    """
+    
+    def __init__(self, 
+                 aggregation_method: str = "maxsim",
+                 spatial_clustering: bool = True,
+                 cluster_distance_threshold: float = 100.0):
+        """
+        Initialize the aggregator.
+        
+        Args:
+            aggregation_method: "maxsim", "voting", "weighted", or "mean"
+            spatial_clustering: Whether to cluster spatially close patches
+            cluster_distance_threshold: Distance threshold for spatial clustering
+        """
+        self.aggregation_method = aggregation_method
+        self.spatial_clustering = spatial_clustering
+        self.cluster_distance_threshold = cluster_distance_threshold
+    
+    def aggregate_results(self, 
+                         search_results: List[Dict[str, Any]], 
+                         top_k: int = 10) -> List[AggregatedResult]:
+        """
+        Aggregate patch-level search results into document-level results.
+        
+        Args:
+            search_results: List of search results from LeannSearcher
+            top_k: Number of top documents to return
+            
+        Returns:
+            List of aggregated document results
+        """
+        # Group results by image
+        image_groups = defaultdict(list)
+        
+        for result in search_results:
+            metadata = result.metadata
+            if "image_name" in metadata and "patch_id" in metadata:
+                patch_result = PatchResult(
+                    patch_id=metadata["patch_id"],
+                    image_name=metadata["image_name"],
+                    image_path=metadata["image_path"],
+                    coordinates=tuple(metadata["coordinates"]),
+                    score=result.score,
+                    attention_score=metadata.get("attention_score", 0.0),
+                    scale=metadata.get("scale", 1.0),
+                    metadata=metadata
+                )
+                image_groups[metadata["image_name"]].append(patch_result)
+        
+        # Aggregate each image group
+        aggregated_results = []
+        for image_name, patches in image_groups.items():
+            if len(patches) == 0:
+                continue
+                
+            agg_result = self._aggregate_image_patches(image_name, patches)
+            aggregated_results.append(agg_result)
+        
+        # Sort by aggregated score and return top-k
+        aggregated_results.sort(key=lambda x: x.doc_score, reverse=True)
+        return aggregated_results[:top_k]
+    
+    def _aggregate_image_patches(self, image_name: str, patches: List[PatchResult]) -> AggregatedResult:
+        """Aggregate patches for a single image."""
+        
+        if self.aggregation_method == "maxsim":
+            doc_score = max(patch.score for patch in patches)
+            best_patch = max(patches, key=lambda p: p.score)
+            
+        elif self.aggregation_method == "voting":
+            # Count patches above threshold
+            threshold = np.percentile([p.score for p in patches], 75)
+            doc_score = sum(1 for patch in patches if patch.score >= threshold)
+            best_patch = max(patches, key=lambda p: p.score)
+            
+        elif self.aggregation_method == "weighted":
+            # Weight by attention scores
+            total_weighted_score = sum(p.score * p.attention_score for p in patches)
+            total_weights = sum(p.attention_score for p in patches)
+            doc_score = total_weighted_score / max(total_weights, 1e-8)
+            best_patch = max(patches, key=lambda p: p.score * p.attention_score)
+            
+        elif self.aggregation_method == "mean":
+            doc_score = np.mean([patch.score for patch in patches])
+            best_patch = max(patches, key=lambda p: p.score)
+            
+        else:
+            raise ValueError(f"Unknown aggregation method: {self.aggregation_method}")
+        
+        # Spatial clustering if enabled
+        spatial_clusters = None
+        if self.spatial_clustering:
+            spatial_clusters = self._cluster_patches_spatially(patches)
+        
+        return AggregatedResult(
+            image_name=image_name,
+            image_path=patches[0].image_path,
+            doc_score=float(doc_score),
+            patch_count=len(patches),
+            best_patch=best_patch,
+            all_patches=sorted(patches, key=lambda p: p.score, reverse=True),
+            aggregation_method=self.aggregation_method,
+            spatial_clusters=spatial_clusters
+        )
+    
+    def _cluster_patches_spatially(self, patches: List[PatchResult]) -> List[List[PatchResult]]:
+        """Cluster patches that are spatially close to each other."""
+        if len(patches) <= 1:
+            return [patches]
+        
+        clusters = []
+        remaining_patches = patches.copy()
+        
+        while remaining_patches:
+            # Start new cluster with highest scoring remaining patch
+            seed_patch = max(remaining_patches, key=lambda p: p.score)
+            current_cluster = [seed_patch]
+            remaining_patches.remove(seed_patch)
+            
+            # Add nearby patches to cluster
+            added_to_cluster = True
+            while added_to_cluster:
+                added_to_cluster = False
+                for patch in remaining_patches.copy():
+                    if self._is_patch_nearby(patch, current_cluster):
+                        current_cluster.append(patch)
+                        remaining_patches.remove(patch)
+                        added_to_cluster = True
+            
+            clusters.append(current_cluster)
+        
+        return sorted(clusters, key=lambda cluster: max(p.score for p in cluster), reverse=True)
+    
+    def _is_patch_nearby(self, patch: PatchResult, cluster: List[PatchResult]) -> bool:
+        """Check if a patch is spatially close to any patch in the cluster."""
+        patch_center = self._get_patch_center(patch.coordinates)
+        
+        for cluster_patch in cluster:
+            cluster_center = self._get_patch_center(cluster_patch.coordinates)
+            distance = np.sqrt((patch_center[0] - cluster_center[0])**2 + 
+                             (patch_center[1] - cluster_center[1])**2)
+            
+            if distance <= self.cluster_distance_threshold:
+                return True
+        
+        return False
+    
+    def _get_patch_center(self, coordinates: Tuple[int, int, int, int]) -> Tuple[float, float]:
+        """Get center point of a patch."""
+        x1, y1, x2, y2 = coordinates
+        return ((x1 + x2) / 2, (y1 + y2) / 2)
+    
+    def print_aggregated_results(self, results: List[AggregatedResult], max_patches_per_doc: int = 3):
+        """Pretty print aggregated results."""
+        print(f"\n🔍 Aggregated Results (method: {self.aggregation_method})")
+        print("=" * 80)
+        
+        for i, result in enumerate(results):
+            print(f"\n{i+1}. {result.image_name}")
+            print(f"   Doc Score: {result.doc_score:.4f} | Patches: {result.patch_count}")
+            print(f"   Path: {result.image_path}")
+            
+            # Show best patch
+            best = result.best_patch
+            print(f"   🌟 Best Patch: #{best.patch_id} at {best.coordinates} (score: {best.score:.4f})")
+            
+            # Show top patches
+            print(f"   📍 Top Patches:")
+            for j, patch in enumerate(result.all_patches[:max_patches_per_doc]):
+                print(f"      {j+1}. Patch #{patch.patch_id}: {patch.score:.4f} at {patch.coordinates}")
+            
+            # Show spatial clusters if available
+            if result.spatial_clusters and len(result.spatial_clusters) > 1:
+                print(f"   🗂️ Spatial Clusters: {len(result.spatial_clusters)}")
+                for j, cluster in enumerate(result.spatial_clusters[:2]):  # Show top 2 clusters
+                    cluster_score = max(p.score for p in cluster)
+                    print(f"      Cluster {j+1}: {len(cluster)} patches (best: {cluster_score:.4f})")
+
+def demo_aggregation():
+    """Demonstrate the multi-vector aggregation functionality."""
+    print("=== Multi-Vector Aggregation Demo ===")
+    
+    # Simulate some patch-level search results
+    # In real usage, these would come from LeannSearcher.search()
+    
+    class MockResult:
+        def __init__(self, score, metadata):
+            self.score = score
+            self.metadata = metadata
+    
+    # Simulate results for 2 images with multiple patches each
+    mock_results = [
+        # Image 1: cats_and_kitchen.jpg - 4 patches
+        MockResult(0.85, {
+            "image_name": "cats_and_kitchen.jpg",
+            "image_path": "/path/to/cats_and_kitchen.jpg",
+            "patch_id": 3,
+            "coordinates": [100, 50, 224, 174],  # Kitchen area
+            "attention_score": 0.92,
+            "scale": 1.0
+        }),
+        MockResult(0.78, {
+            "image_name": "cats_and_kitchen.jpg", 
+            "image_path": "/path/to/cats_and_kitchen.jpg",
+            "patch_id": 7,
+            "coordinates": [200, 300, 324, 424],  # Cat area
+            "attention_score": 0.88,
+            "scale": 1.0
+        }),
+        MockResult(0.72, {
+            "image_name": "cats_and_kitchen.jpg",
+            "image_path": "/path/to/cats_and_kitchen.jpg", 
+            "patch_id": 12,
+            "coordinates": [150, 100, 274, 224],  # Appliances
+            "attention_score": 0.75,
+            "scale": 1.0
+        }),
+        MockResult(0.65, {
+            "image_name": "cats_and_kitchen.jpg",
+            "image_path": "/path/to/cats_and_kitchen.jpg",
+            "patch_id": 15,
+            "coordinates": [50, 250, 174, 374],  # Furniture
+            "attention_score": 0.70,
+            "scale": 1.0
+        }),
+        
+        # Image 2: city_street.jpg - 3 patches  
+        MockResult(0.68, {
+            "image_name": "city_street.jpg",
+            "image_path": "/path/to/city_street.jpg",
+            "patch_id": 2,
+            "coordinates": [300, 100, 424, 224],  # Buildings
+            "attention_score": 0.80,
+            "scale": 1.0
+        }),
+        MockResult(0.62, {
+            "image_name": "city_street.jpg",
+            "image_path": "/path/to/city_street.jpg",
+            "patch_id": 8,
+            "coordinates": [100, 350, 224, 474],  # Street level
+            "attention_score": 0.75,
+            "scale": 1.0
+        }),
+        MockResult(0.55, {
+            "image_name": "city_street.jpg", 
+            "image_path": "/path/to/city_street.jpg",
+            "patch_id": 11,
+            "coordinates": [400, 200, 524, 324],  # Sky area
+            "attention_score": 0.60,
+            "scale": 1.0
+        }),
+    ]
+    
+    # Test different aggregation methods
+    methods = ["maxsim", "voting", "weighted", "mean"]
+    
+    for method in methods:
+        print(f"\n{'='*20} {method.upper()} AGGREGATION {'='*20}")
+        
+        aggregator = MultiVectorAggregator(
+            aggregation_method=method,
+            spatial_clustering=True,
+            cluster_distance_threshold=100.0
+        )
+        
+        aggregated = aggregator.aggregate_results(mock_results, top_k=5)
+        aggregator.print_aggregated_results(aggregated)
+
+if __name__ == "__main__":
+    demo_aggregation()

examples/run_evaluation.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+"""
+This script runs a recall evaluation on a given LEANN index.
+It correctly compares results by fetching the text content for both the new search
+results and the golden standard results, making the comparison robust to ID changes.
+"""
+
+import json
+import argparse
+import time
+from pathlib import Path
+import sys
+import numpy as np
+from typing import List, Dict, Any
+import glob
+import pickle
+
+# Add project root to path to allow importing from leann
+project_root = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(project_root))
+
+from leann.api import LeannSearcher
+
+# --- Configuration ---
+NQ_QUERIES_FILE = Path("/opt/dlami/nvme/scaling_out/examples/nq_open.jsonl")
+
+# Ground truth files for different datasets
+GROUND_TRUTH_FILES = {
+    "rpj_wiki": "/opt/dlami/nvme/scaling_out/indices/rpj_wiki/facebook/contriever-msmarco/flat_results_nq_k3.json",
+    "dpr": "/opt/dlami/nvme/scaling_out/indices/dpr/facebook/contriever-msmarco/flat_results_nq_k3.json"
+}
+
+# Old passages for different datasets  
+OLD_PASSAGES_GLOBS = {
+    "rpj_wiki": "/opt/dlami/nvme/scaling_out/passages/rpj_wiki/8-shards/raw_passages-*-of-8.pkl.jsonl",
+    "dpr": "/opt/dlami/nvme/scaling_out/passages/dpr/1-shards/raw_passages-*-of-1.pkl.jsonl"
+}
+
+# --- Helper Class to Load Original Passages ---
+class OldPassageLoader:
+    """A simplified version of the old LazyPassages class to fetch golden results by ID."""
+    def __init__(self, passages_glob: str):
+        self.jsonl_paths = sorted(glob.glob(passages_glob))
+        self.offsets = {}
+        self.fps = [open(p, "r", encoding="utf-8") for p in self.jsonl_paths]
+        print("Building offset map for original passages...")
+        for i, shard_path_str in enumerate(self.jsonl_paths):
+            old_idx_path = Path(shard_path_str.replace(".jsonl", ".idx"))
+            if not old_idx_path.exists(): continue
+            with open(old_idx_path, 'rb') as f:
+                shard_offsets = pickle.load(f)
+                for pid, offset in shard_offsets.items():
+                    self.offsets[str(pid)] = (i, offset)
+        print("Offset map for original passages is ready.")
+
+    def get_passage_by_id(self, pid: str) -> Dict[str, Any]:
+        pid = str(pid)
+        if pid not in self.offsets:
+            raise ValueError(f"Passage ID {pid} not found in offsets")
+        file_idx, offset = self.offsets[pid]
+        fp = self.fps[file_idx]
+        fp.seek(offset)
+        return json.loads(fp.readline())
+
+    def __del__(self):
+        for fp in self.fps:
+            fp.close()
+
+def load_queries(file_path: Path) -> List[str]:
+    queries = []
+    with open(file_path, 'r', encoding='utf-8') as f:
+        for line in f:
+            data = json.loads(line)
+            queries.append(data['query'])
+    return queries
+
+def main():
+    parser = argparse.ArgumentParser(description="Run recall evaluation on a LEANN index.")
+    parser.add_argument("index_path", type=str, help="Path to the LEANN index to evaluate.")
+    parser.add_argument("--num-queries", type=int, default=10, help="Number of queries to evaluate.")
+    parser.add_argument("--top-k", type=int, default=3, help="The 'k' value for recall@k.")
+    parser.add_argument("--ef-search", type=int, default=120, help="The 'efSearch' parameter for HNSW.")
+    args = parser.parse_args()
+
+    print(f"--- Final, Correct Recall Evaluation (efSearch={args.ef_search}) ---")
+    
+    # Detect dataset type from index path
+    index_path_str = str(args.index_path)
+    if "rpj_wiki" in index_path_str:
+        dataset_type = "rpj_wiki"
+    elif "dpr" in index_path_str:
+        dataset_type = "dpr"
+    else:
+        print("WARNING: Unknown dataset type, defaulting to rpj_wiki")
+        dataset_type = "rpj_wiki"
+    
+    print(f"INFO: Detected dataset type: {dataset_type}")
+
+    try:
+        searcher = LeannSearcher(args.index_path)
+        queries = load_queries(NQ_QUERIES_FILE)
+        
+        golden_results_file = GROUND_TRUTH_FILES[dataset_type]
+        old_passages_glob = OLD_PASSAGES_GLOBS[dataset_type]
+        
+        print(f"INFO: Using ground truth file: {golden_results_file}")
+        print(f"INFO: Using old passages glob: {old_passages_glob}")
+        
+        with open(golden_results_file, 'r') as f:
+            golden_results_data = json.load(f)
+        
+        old_passage_loader = OldPassageLoader(old_passages_glob)
+
+        num_eval_queries = min(args.num_queries, len(queries))
+        queries = queries[:num_eval_queries]
+        
+        print(f"\nRunning evaluation on {num_eval_queries} queries...")
+        recall_scores = []
+        search_times = []
+
+        for i in range(num_eval_queries):
+            start_time = time.time()
+            new_results = searcher.search(queries[i], top_k=args.top_k, ef=args.ef_search)
+            search_times.append(time.time() - start_time)
+
+            # Correct Recall Calculation: Based on TEXT content
+            new_texts = {result.text for result in new_results}
+            golden_ids = golden_results_data["indices"][i][:args.top_k]
+            golden_texts = {old_passage_loader.get_passage_by_id(str(gid))['text'] for gid in golden_ids}
+
+            overlap = len(new_texts & golden_texts)
+            recall = overlap / len(golden_texts) if golden_texts else 0
+            recall_scores.append(recall)
+
+            print("\n--- EVALUATION RESULTS ---")
+            print(f"Query: {queries[i]}")
+            print(f"New Results: {new_texts}")
+            print(f"Golden Results: {golden_texts}")
+            print(f"Overlap: {overlap}")
+            print(f"Recall: {recall}")
+            print(f"Search Time: {search_times[-1]:.4f}s")
+            print(f"--------------------------------")
+
+        avg_recall = np.mean(recall_scores) if recall_scores else 0
+        avg_time = np.mean(search_times) if search_times else 0
+
+        print(f"\n🎉 --- Evaluation Complete ---")
+        print(f"Avg. Recall@{args.top_k} (efSearch={args.ef_search}): {avg_recall:.4f}")
+        print(f"Avg. Search Time: {avg_time:.4f}s")
+
+    except Exception as e:
+        print(f"\n❌ An error occurred during evaluation: {e}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    main()

packages/leann-backend-diskann/leann_backend_diskann/diskann_backend.py

@@ -3,29 +3,25 @@ import os
 import json
 import struct
 from pathlib import Path
-from typing import Dict, Any
+from typing import Dict, Any, List
 import contextlib
-import threading
+import pickle
-import time
-import atexit
-import socket
-import subprocess
-import sys
 
-from leann.embedding_server_manager import EmbeddingServerManager
+from leann.searcher_base import BaseSearcher
 from leann.registry import register_backend
 from leann.interface import (
     LeannBackendFactoryInterface,
     LeannBackendBuilderInterface,
     LeannBackendSearcherInterface
 )
-from . import _diskannpy as diskannpy
 
-METRIC_MAP = {
+def _get_diskann_metrics():
+    from . import _diskannpy as diskannpy
+    return {
         "mips": diskannpy.Metric.INNER_PRODUCT,
         "l2": diskannpy.Metric.L2,
         "cosine": diskannpy.Metric.COSINE,
-}
+    }
 
 @contextlib.contextmanager
 def chdir(path):
@@ -51,210 +47,87 @@ class DiskannBackend(LeannBackendFactoryInterface):
 
     @staticmethod
     def searcher(index_path: str, **kwargs) -> LeannBackendSearcherInterface:
-        path = Path(index_path)
-        meta_path = path.parent / f"{path.name}.meta.json"
-        if not meta_path.exists():
-            raise FileNotFoundError(f"Leann metadata file not found at {meta_path}.")
-
-        with open(meta_path, 'r') as f:
-            meta = json.load(f)
-
-        # Pass essential metadata to the searcher
-        kwargs['meta'] = meta
         return DiskannSearcher(index_path, **kwargs)
 
 class DiskannBuilder(LeannBackendBuilderInterface):
     def __init__(self, **kwargs):
         self.build_params = kwargs
 
-    def _generate_passages_file(self, index_dir: Path, index_prefix: str, **kwargs):
+    def build(self, data: np.ndarray, ids: List[str], index_path: str, **kwargs):
-        """Generate passages file for recompute mode, mirroring HNSW backend."""
-        try:
-            chunks = kwargs.get('chunks', [])
-            if not chunks:
-                print("INFO: No chunks data provided, skipping passages file generation for DiskANN.")
-                return
-            
-            passages_data = {str(node_id): chunk["text"] for node_id, chunk in enumerate(chunks)}
-            
-            passages_file = index_dir / f"{index_prefix}.passages.json"
-            with open(passages_file, 'w', encoding='utf-8') as f:
-                json.dump(passages_data, f, ensure_ascii=False, indent=2)
-            
-            print(f"✅ Generated passages file for recompute mode at '{passages_file}' ({len(passages_data)} passages)")
-            
-        except Exception as e:
-            print(f"💥 ERROR: Failed to generate passages file for DiskANN. Exception: {e}")
-            pass
-
-    def build(self, data: np.ndarray, index_path: str, **kwargs):
         path = Path(index_path)
         index_dir = path.parent
         index_prefix = path.stem
-
         index_dir.mkdir(parents=True, exist_ok=True)
 
         if data.dtype != np.float32:
             data = data.astype(np.float32)
-        if not data.flags['C_CONTIGUOUS']:
-            data = np.ascontiguousarray(data)
 
         data_filename = f"{index_prefix}_data.bin"
         _write_vectors_to_bin(data, index_dir / data_filename)
 
+        label_map = {i: str_id for i, str_id in enumerate(ids)}
+        label_map_file = index_dir / "leann.labels.map"
+        with open(label_map_file, 'wb') as f:
+            pickle.dump(label_map, f)
+
         build_kwargs = {**self.build_params, **kwargs}
-        metric_str = build_kwargs.get("distance_metric", "mips").lower()
+        metric_enum = _get_diskann_metrics().get(build_kwargs.get("distance_metric", "mips").lower())
-        metric_enum = METRIC_MAP.get(metric_str)
         if metric_enum is None:
-            raise ValueError(f"Unsupported distance_metric '{metric_str}'.")
+            raise ValueError(f"Unsupported distance_metric.")
-
-        complexity = build_kwargs.get("complexity", 64)
-        graph_degree = build_kwargs.get("graph_degree", 32)
-        final_index_ram_limit = build_kwargs.get("search_memory_maximum", 4.0)
-        indexing_ram_budget = build_kwargs.get("build_memory_maximum", 8.0)
-        num_threads = build_kwargs.get("num_threads", 8)
-        pq_disk_bytes = build_kwargs.get("pq_disk_bytes", 0)
-        codebook_prefix = ""
-        is_recompute = build_kwargs.get("is_recompute", False)
-
-        print(f"INFO: Building DiskANN index for {data.shape[0]} vectors with metric {metric_enum}...")
 
         try:
+            from . import _diskannpy as diskannpy
             with chdir(index_dir):
                 diskannpy.build_disk_float_index(
-                    metric_enum,
+                    metric_enum, data_filename, index_prefix,
-                    data_filename,
+                    build_kwargs.get("complexity", 64), build_kwargs.get("graph_degree", 32),
-                    index_prefix,
+                    build_kwargs.get("search_memory_maximum", 4.0), build_kwargs.get("build_memory_maximum", 8.0),
-                    complexity,
+                    build_kwargs.get("num_threads", 8), build_kwargs.get("pq_disk_bytes", 0), ""
-                    graph_degree,
-                    final_index_ram_limit,
-                    indexing_ram_budget,
-                    num_threads,
-                    pq_disk_bytes,
-                    codebook_prefix
                 )
-            print(f"✅ DiskANN index built successfully at '{index_dir / index_prefix}'")
-            if is_recompute:
-                self._generate_passages_file(index_dir, index_prefix, **build_kwargs)
-        except Exception as e:
-            print(f"💥 ERROR: DiskANN index build failed. Exception: {e}")
-            raise
         finally:
             temp_data_file = index_dir / data_filename
             if temp_data_file.exists():
                 os.remove(temp_data_file)
 
-class DiskannSearcher(LeannBackendSearcherInterface):
+class DiskannSearcher(BaseSearcher):
     def __init__(self, index_path: str, **kwargs):
-        self.meta = kwargs.get("meta", {})
+        super().__init__(index_path, backend_module_name="leann_backend_diskann.embedding_server", **kwargs)
-        if not self.meta:
+        from . import _diskannpy as diskannpy
-            raise ValueError("DiskannSearcher requires metadata from .meta.json.")
 
-        dimensions = self.meta.get("dimensions")
+        distance_metric = kwargs.get("distance_metric", "mips").lower()
-        if not dimensions:
+        metric_enum = _get_diskann_metrics().get(distance_metric)
-            raise ValueError("Dimensions not found in Leann metadata.")
-        
-        self.distance_metric = self.meta.get("distance_metric", "mips").lower()
-        metric_enum = METRIC_MAP.get(self.distance_metric)
         if metric_enum is None:
-            raise ValueError(f"Unsupported distance_metric '{self.distance_metric}'.")
+            raise ValueError(f"Unsupported distance_metric '{distance_metric}'.")
 
-        self.embedding_model = self.meta.get("embedding_model")
+        self.num_threads = kwargs.get("num_threads", 8)
-        if not self.embedding_model:
-            print("WARNING: embedding_model not found in meta.json. Recompute will fail if attempted.")
-
-        path = Path(index_path)
-        self.index_dir = path.parent
-        self.index_prefix = path.stem
-        
-        num_threads = kwargs.get("num_threads", 8)
-        num_nodes_to_cache = kwargs.get("num_nodes_to_cache", 0)
         self.zmq_port = kwargs.get("zmq_port", 6666)
 
-        try:
+        full_index_prefix = str(self.index_dir / self.index_path.stem)
-            full_index_prefix = str(self.index_dir / self.index_prefix)
         self._index = diskannpy.StaticDiskFloatIndex(
-                metric_enum, full_index_prefix, num_threads, num_nodes_to_cache, 1, self.zmq_port, "", ""
+            metric_enum, full_index_prefix, self.num_threads, 
+            kwargs.get("num_nodes_to_cache", 0), 1, self.zmq_port, "", ""
         )
-            self.num_threads = num_threads
-            self.embedding_server_manager = EmbeddingServerManager(
-                backend_module_name="leann_backend_diskann.embedding_server"
-            )
-            print("✅ DiskANN index loaded successfully.")
-        except Exception as e:
-            print(f"💥 ERROR: Failed to load DiskANN index. Exception: {e}")
-            raise
 
     def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, Any]:
-        complexity = kwargs.get("complexity", 256)
+        recompute = kwargs.get("recompute_beighbor_embeddings", False)
-        beam_width = kwargs.get("beam_width", 4)
+        if recompute:
-        
+            meta_file_path = self.index_dir / f"{self.index_path.name}.meta.json"
-        USE_DEFERRED_FETCH = kwargs.get("USE_DEFERRED_FETCH", False)
+            if not meta_file_path.exists():
-        skip_search_reorder = kwargs.get("skip_search_reorder", False)
+                raise RuntimeError(f"FATAL: Recompute mode enabled but metadata file not found: {meta_file_path}")
-        recompute_beighbor_embeddings = kwargs.get("recompute_beighbor_embeddings", False)
+            zmq_port = kwargs.get("zmq_port", self.zmq_port)
-        dedup_node_dis = kwargs.get("dedup_node_dis", False)
+            self._ensure_server_running(str(meta_file_path), port=zmq_port, **kwargs)
-        prune_ratio = kwargs.get("prune_ratio", 0.0)
-        batch_recompute = kwargs.get("batch_recompute", False)
-        global_pruning = kwargs.get("global_pruning", False)
-        port = kwargs.get("zmq_port", self.zmq_port)
-        
-        if recompute_beighbor_embeddings:
-            print(f"INFO: DiskANN ZMQ mode enabled - ensuring embedding server is running")
-            if not self.embedding_model:
-                raise ValueError("Cannot use recompute_beighbor_embeddings without 'embedding_model' in meta.json.")
-
-            passages_file = kwargs.get("passages_file")
-            if not passages_file:
-                potential_passages_file = self.index_dir / f"{self.index_prefix}.passages.json"
-                if potential_passages_file.exists():
-                    passages_file = str(potential_passages_file)
-                    print(f"INFO: Automatically found passages file: {passages_file}")
-
-            if not passages_file:
-                raise RuntimeError(
-                    f"Recompute mode is enabled, but no passages file was found. "
-                    f"A '{self.index_prefix}.passages.json' file should exist in the index directory "
-                    f"'{self.index_dir}'. Ensure you build the index with 'recompute=True'."
-                )
-
-            server_started = self.embedding_server_manager.start_server(
-                port=self.zmq_port,
-                model_name=self.embedding_model,
-                distance_metric=self.distance_metric,
-                passages_file=passages_file
-            )
-            
-            if not server_started:
-                raise RuntimeError(f"Failed to start DiskANN embedding server on port {self.zmq_port}")
 
         if query.dtype != np.float32:
             query = query.astype(np.float32)
-        if query.ndim == 1:
-            query = np.expand_dims(query, axis=0)
 
-        try:
         labels, distances = self._index.batch_search(
-                query,
+            query, query.shape[0], top_k,
-                query.shape[0],
+            kwargs.get("complexity", 256), kwargs.get("beam_width", 4), self.num_threads,
-                top_k,
+            kwargs.get("USE_DEFERRED_FETCH", False), kwargs.get("skip_search_reorder", False),
-                complexity,
+            recompute, kwargs.get("dedup_node_dis", False), kwargs.get("prune_ratio", 0.0),
-                beam_width,
+            kwargs.get("batch_recompute", False), kwargs.get("global_pruning", False)
-                self.num_threads,
-                USE_DEFERRED_FETCH,
-                skip_search_reorder,
-                recompute_beighbor_embeddings,
-                dedup_node_dis,
-                prune_ratio,
-                batch_recompute,
-                global_pruning
         )
-            return {"labels": labels, "distances": distances}
-        except Exception as e:
-            print(f"💥 ERROR: DiskANN search failed. Exception: {e}")
-            batch_size = query.shape[0]
-            return {"labels": np.full((batch_size, top_k), -1, dtype=np.int64), 
-                   "distances": np.full((batch_size, top_k), float('inf'), dtype=np.float32)}
 
-    def __del__(self):
+        string_labels = [[self.label_map.get(int_label, f"unknown_{int_label}") for int_label in batch_labels] for batch_labels in labels]
-        if hasattr(self, 'embedding_server_manager'):
+
-            self.embedding_server_manager.stop_server()
+        return {"labels": string_labels, "distances": distances}

packages/leann-backend-diskann/leann_backend_diskann/embedding_server.py

@@ -15,6 +15,8 @@ import os
 from contextlib import contextmanager
 import zmq
 import numpy as np
+from pathlib import Path
+import pickle
 
 RED = "\033[91m"
 RESET = "\033[0m"
@@ -39,23 +41,113 @@ class SimplePassageLoader:
     def __len__(self) -> int:
         return len(self.passages_data)
 
-def load_passages_from_file(passages_file: str) -> SimplePassageLoader:
+def load_passages_from_metadata(meta_file: str) -> SimplePassageLoader:
     """
-    Load passages from a JSON file
+    Load passages using metadata file with PassageManager for lazy loading
-    Expected format: {"passage_id": "passage_text", ...}
     """
-    if not os.path.exists(passages_file):
+    # Load metadata to get passage sources
-        print(f"Warning: Passages file {passages_file} not found. Using empty loader.")
+    with open(meta_file, 'r') as f:
-        return SimplePassageLoader()
+        meta = json.load(f)
+    
+    # Import PassageManager dynamically to avoid circular imports
+    import sys
+    from pathlib import Path
+    
+    # Find the leann package directory relative to this file
+    current_dir = Path(__file__).parent
+    leann_core_path = current_dir.parent.parent / "leann-core" / "src"
+    sys.path.insert(0, str(leann_core_path))
     
     try:
-        with open(passages_file, 'r', encoding='utf-8') as f:
+        from leann.api import PassageManager
-            passages_data = json.load(f)
+        passage_manager = PassageManager(meta['passage_sources'])
-        print(f"Loaded {len(passages_data)} passages from {passages_file}")
+    finally:
-        return SimplePassageLoader(passages_data)
+        sys.path.pop(0)
+    
+    # Load label map 
+    passages_dir = Path(meta_file).parent
+    label_map_file = passages_dir / "leann.labels.map"
+    
+    if label_map_file.exists():
+        import pickle
+        with open(label_map_file, 'rb') as f:
+            label_map = pickle.load(f)
+        print(f"Loaded label map with {len(label_map)} entries")
+    else:
+        raise FileNotFoundError(f"Label map file not found: {label_map_file}")
+    
+    print(f"Initialized lazy passage loading for {len(label_map)} passages")
+    
+    class LazyPassageLoader(SimplePassageLoader):
+        def __init__(self, passage_manager, label_map):
+            self.passage_manager = passage_manager
+            self.label_map = label_map
+            # Initialize parent with empty data
+            super().__init__({})
+        
+        def __getitem__(self, passage_id: Union[str, int]) -> Dict[str, str]:
+            """Get passage by ID with lazy loading"""
+            try:
+                int_id = int(passage_id)
+                if int_id in self.label_map:
+                    string_id = self.label_map[int_id]
+                    passage_data = self.passage_manager.get_passage(string_id)
+                    if passage_data and passage_data.get("text"):
+                        return {"text": passage_data["text"]}
+                    else:
+                        raise RuntimeError(f"FATAL: Empty text for ID {int_id} -> {string_id}")
+                else:
+                    raise RuntimeError(f"FATAL: ID {int_id} not found in label_map")
             except Exception as e:
-        print(f"Error loading passages from {passages_file}: {e}")
+                raise RuntimeError(f"FATAL: Exception getting passage {passage_id}: {e}")
-        return SimplePassageLoader()
+        
+        def __len__(self) -> int:
+            return len(self.label_map)
+    
+    return LazyPassageLoader(passage_manager, label_map)
+
+def load_passages_from_file(passages_file: str) -> SimplePassageLoader:
+    """
+    Load passages from a JSONL file with label map support
+    Expected format: {"id": "passage_id", "text": "passage_text", "metadata": {...}} (one per line)
+    """
+    
+    if not os.path.exists(passages_file):
+        raise FileNotFoundError(f"Passages file {passages_file} not found.")
+    
+    if not passages_file.endswith('.jsonl'):
+        raise ValueError(f"Expected .jsonl file format, got: {passages_file}")
+    
+    # Load label map (int -> string_id)
+    passages_dir = Path(passages_file).parent
+    label_map_file = passages_dir / "leann.labels.map"
+    
+    label_map = {}
+    if label_map_file.exists():
+        with open(label_map_file, 'rb') as f:
+            label_map = pickle.load(f)
+        print(f"Loaded label map with {len(label_map)} entries")
+    else:
+        raise FileNotFoundError(f"Label map file not found: {label_map_file}")
+    
+    # Load passages by string ID
+    string_id_passages = {}
+    with open(passages_file, 'r', encoding='utf-8') as f:
+        for line in f:
+            if line.strip():
+                passage = json.loads(line)
+                string_id_passages[passage['id']] = passage['text']
+    
+    # Create int ID -> text mapping using label map
+    passages_data = {}
+    for int_id, string_id in label_map.items():
+        if string_id in string_id_passages:
+            passages_data[str(int_id)] = string_id_passages[string_id]
+        else:
+            print(f"WARNING: String ID {string_id} from label map not found in passages")
+    
+    print(f"Loaded {len(passages_data)} passages from JSONL file {passages_file} using label map")
+    return SimplePassageLoader(passages_data)
 
 def create_embedding_server_thread(
     zmq_port=5555,
@@ -113,6 +205,19 @@ def create_embedding_server_thread(
 
         # Load passages from file if provided
         if passages_file and os.path.exists(passages_file):
+            # Check if it's a metadata file or a single passages file
+            if passages_file.endswith('.meta.json'):
+                passages = load_passages_from_metadata(passages_file)
+            else:
+                # Try to find metadata file in same directory
+                passages_dir = Path(passages_file).parent
+                meta_files = list(passages_dir.glob("*.meta.json"))
+                if meta_files:
+                    print(f"Found metadata file: {meta_files[0]}, using lazy loading")
+                    passages = load_passages_from_metadata(str(meta_files[0]))
+                else:
+                    # Fallback to original single file loading (will cause warnings)
+                    print("WARNING: No metadata file found, using single file loading (may cause missing passage warnings)")
                     passages = load_passages_from_file(passages_file)
         else:
             print("WARNING: No passages file provided or file not found. Using an empty passage loader.")

packages/leann-backend-hnsw/leann_backend_hnsw/convert_to_csr.py

@@ -468,16 +468,27 @@ def convert_hnsw_graph_to_csr(input_filename, output_filename, prune_embeddings=
             # --- Write CSR HNSW graph data using unified function ---
             print(f"[{time.time() - start_time:.2f}s] Writing CSR HNSW graph data in FAISS-compatible order...")
             
-            # Determine storage fourcc based on prune_embeddings
+            # Determine storage fourcc and data based on prune_embeddings
-            output_storage_fourcc = NULL_INDEX_FOURCC if prune_embeddings else (storage_fourcc if 'storage_fourcc' in locals() else NULL_INDEX_FOURCC)
             if prune_embeddings:
                 print(f"   Pruning embeddings: Writing NULL storage marker.")
+                output_storage_fourcc = NULL_INDEX_FOURCC
+                storage_data = b''
+            else:
+                # Keep embeddings - read and preserve original storage data
+                if storage_fourcc and storage_fourcc != NULL_INDEX_FOURCC:
+                    print(f"   Preserving embeddings: Reading original storage data...")
+                    storage_data = f_in.read()  # Read remaining storage data
+                    output_storage_fourcc = storage_fourcc
+                    print(f"   Read {len(storage_data)} bytes of storage data")
+                else:
+                    print(f"   No embeddings found in original file (NULL storage)")
+                    output_storage_fourcc = NULL_INDEX_FOURCC
                     storage_data = b''
             
             # Use the unified write function
             write_compact_format(f_out, original_hnsw_data, assign_probas_np, cum_nneighbor_per_level_np, 
                                levels_np, compact_level_ptr, compact_node_offsets_np, 
-                               compact_neighbors_data, output_storage_fourcc, storage_data if not prune_embeddings else b'')
+                               compact_neighbors_data, output_storage_fourcc, storage_data)
             
             # Clean up memory
             del assign_probas_np, cum_nneighbor_per_level_np, levels_np

packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py

@@ -1,18 +1,12 @@
 import numpy as np
 import os
 import json
-import struct
 from pathlib import Path
-from typing import Dict, Any
+from typing import Dict, Any, List
-import contextlib
+import pickle
-import threading
+import shutil
-import time
-import atexit
-import socket
-import subprocess
-import sys
 
-from leann.embedding_server_manager import EmbeddingServerManager
+from leann.searcher_base import BaseSearcher
 from .convert_to_csr import convert_hnsw_graph_to_csr
 
 from leann.registry import register_backend
@@ -38,97 +32,53 @@ class HNSWBackend(LeannBackendFactoryInterface):
 
     @staticmethod
     def searcher(index_path: str, **kwargs) -> LeannBackendSearcherInterface:
-        path = Path(index_path)
-        meta_path = path.parent / f"{path.name}.meta.json"
-        if not meta_path.exists():
-            raise FileNotFoundError(f"Leann metadata file not found at {meta_path}.")
-        
-        with open(meta_path, 'r') as f:
-            meta = json.load(f)
-
-        kwargs['meta'] = meta
         return HNSWSearcher(index_path, **kwargs)
 
 class HNSWBuilder(LeannBackendBuilderInterface):
     def __init__(self, **kwargs):
         self.build_params = kwargs.copy()
-        
-        # --- Configuration defaults with standardized names ---
         self.is_compact = self.build_params.setdefault("is_compact", True)
         self.is_recompute = self.build_params.setdefault("is_recompute", True)
-        
-        # --- Additional Options ---
-        self.is_skip_neighbors = self.build_params.setdefault("is_skip_neighbors", False) 
-        self.disk_cache_ratio = self.build_params.setdefault("disk_cache_ratio", 0.0)
-        self.external_storage_path = self.build_params.get("external_storage_path", None)
-        
-        # --- Standard HNSW parameters ---
         self.M = self.build_params.setdefault("M", 32)
         self.efConstruction = self.build_params.setdefault("efConstruction", 200)
         self.distance_metric = self.build_params.setdefault("distance_metric", "mips")
         self.dimensions = self.build_params.get("dimensions")
 
-        if self.is_skip_neighbors and not self.is_compact:
+    def build(self, data: np.ndarray, ids: List[str], index_path: str, **kwargs):
-            raise ValueError("is_skip_neighbors can only be used with is_compact=True")
-        
-        if self.is_recompute and not self.is_compact:
-            raise ValueError("is_recompute requires is_compact=True for efficiency")
-
-    def build(self, data: np.ndarray, index_path: str, **kwargs):
-        """Build HNSW index using FAISS"""
         from . import faiss
-        
         path = Path(index_path)
         index_dir = path.parent
         index_prefix = path.stem
-
         index_dir.mkdir(parents=True, exist_ok=True)
 
         if data.dtype != np.float32:
             data = data.astype(np.float32)
-        if not data.flags['C_CONTIGUOUS']:
-            data = np.ascontiguousarray(data)
 
-        metric_str = self.distance_metric.lower()
+        label_map = {i: str_id for i, str_id in enumerate(ids)}
-        metric_enum = get_metric_map().get(metric_str)
+        label_map_file = index_dir / "leann.labels.map"
+        with open(label_map_file, 'wb') as f:
+            pickle.dump(label_map, f)
+
+        metric_enum = get_metric_map().get(self.distance_metric.lower())
         if metric_enum is None:
-            raise ValueError(f"Unsupported distance_metric '{metric_str}'.")
+            raise ValueError(f"Unsupported distance_metric '{self.distance_metric}'.")
 
-        M = self.M
+        dim = self.dimensions or data.shape[1]
-        efConstruction = self.efConstruction
+        index = faiss.IndexHNSWFlat(dim, self.M, metric_enum)
-        dim = self.dimensions
+        index.hnsw.efConstruction = self.efConstruction
-        if not dim:
-            dim = data.shape[1]
 
-        print(f"INFO: Building HNSW index for {data.shape[0]} vectors with metric {metric_enum}...")
+        if self.distance_metric.lower() == "cosine":
-        
-        try:
-            index = faiss.IndexHNSWFlat(dim, M, metric_enum)
-            index.hnsw.efConstruction = efConstruction
-            
-            if metric_str == "cosine":
             faiss.normalize_L2(data)
 
         index.add(data.shape[0], faiss.swig_ptr(data))
-            
         index_file = index_dir / f"{index_prefix}.index"
         faiss.write_index(index, str(index_file))
 
-            print(f"✅ HNSW index built successfully at '{index_file}'")
-
         if self.is_compact:
             self._convert_to_csr(index_file)
 
-            if self.is_recompute:
-                self._generate_passages_file(index_dir, index_prefix, **kwargs)
-            
-        except Exception as e:
-            print(f"💥 ERROR: HNSW index build failed. Exception: {e}")
-            raise
-
     def _convert_to_csr(self, index_file: Path):
         """Convert built index to CSR format"""
-        try:
         mode_str = "CSR-pruned" if self.is_recompute else "CSR-standard"
         print(f"INFO: Converting HNSW index to {mode_str} format...")
 
@@ -142,8 +92,6 @@ class HNSWBuilder(LeannBackendBuilderInterface):
 
         if success:
             print("✅ CSR conversion successful.")
-                import shutil
-                # rename index_file to index_file.old
             index_file_old = index_file.with_suffix(".old")
             shutil.move(str(index_file), str(index_file_old))
             shutil.move(str(csr_temp_file), str(index_file))
@@ -154,220 +102,53 @@ class HNSWBuilder(LeannBackendBuilderInterface):
                 os.remove(csr_temp_file)
             raise RuntimeError("CSR conversion failed - cannot proceed with compact format")
 
-        except Exception as e:
+class HNSWSearcher(BaseSearcher):
-            print(f"💥 ERROR: CSR conversion failed. Exception: {e}")
-            raise
-
-    def _generate_passages_file(self, index_dir: Path, index_prefix: str, **kwargs):
-        """Generate passages file for recompute mode"""
-        try:
-            chunks = kwargs.get('chunks', [])
-            if not chunks:
-                print("INFO: No chunks data provided, skipping passages file generation")
-                return
-            
-            # Generate node_id to text mapping
-            passages_data = {}
-            for node_id, chunk in enumerate(chunks):
-                passages_data[str(node_id)] = chunk["text"]
-            
-            # Save passages file
-            passages_file = index_dir / f"{index_prefix}.passages.json"
-            with open(passages_file, 'w', encoding='utf-8') as f:
-                json.dump(passages_data, f, ensure_ascii=False, indent=2)
-            
-            print(f"✅ Generated passages file for recompute mode at '{passages_file}' ({len(passages_data)} passages)")
-            
-        except Exception as e:
-            print(f"💥 ERROR: Failed to generate passages file. Exception: {e}")
-            # Don't raise - this is not critical for index building
-            pass
-
-class HNSWSearcher(LeannBackendSearcherInterface):
-    def _get_index_storage_status(self, index_file: Path) -> tuple[bool, bool]:
-        """
-        Robustly determines the index's storage status by parsing the file.
-        
-        Returns:
-            A tuple (is_compact, is_pruned).
-        """
-        if not index_file.exists():
-            return False, False
-        
-        with open(index_file, 'rb') as f:
-            try:
-                def read_struct(fmt):
-                    size = struct.calcsize(fmt)
-                    data = f.read(size)
-                    if len(data) != size:
-                        raise EOFError(f"File ended unexpectedly reading struct fmt '{fmt}'.")
-                    return struct.unpack(fmt, data)[0]
-
-                def skip_vector(element_size):
-                    count = read_struct('<Q')
-                    f.seek(count * element_size, 1)
-
-                # 1. Read up to the compact flag
-                read_struct('<I'); read_struct('<i'); read_struct('<q'); 
-                read_struct('<q'); read_struct('<q'); read_struct('<?')
-                metric_type = read_struct('<i')
-                if metric_type > 1: read_struct('<f')
-                skip_vector(8); skip_vector(4); skip_vector(4)
-                
-                # 2. Check if there's a compact flag byte
-                # Try to read the compact flag, but handle both old and new formats
-                pos_before_compact = f.tell()
-                try:
-                    is_compact = read_struct('<?')
-                    print(f"INFO: Detected is_compact flag as: {is_compact}")
-                except (EOFError, struct.error):
-                    # Old format without compact flag - assume non-compact
-                    f.seek(pos_before_compact)
-                    is_compact = False
-                    print(f"INFO: No compact flag found, assuming is_compact=False")
-
-                # 3. Read storage FourCC to determine if pruned
-                is_pruned = False
-                try:
-                    if is_compact:
-                        # For compact, we need to skip pointers and scalars to get to the storage FourCC
-                        skip_vector(8) # level_ptr
-                        skip_vector(8) # node_offsets
-                        read_struct('<i'); read_struct('<i'); read_struct('<i');
-                        read_struct('<i'); read_struct('<i')
-                        storage_fourcc = read_struct('<I')
-                    else:
-                        # For non-compact, we need to read the flag probe, then skip offsets and neighbors
-                        pos_before_probe = f.tell()
-                        flag_byte = f.read(1)
-                        if not (flag_byte and flag_byte == b'\x00'):
-                            f.seek(pos_before_probe)
-                        skip_vector(8); skip_vector(4) # offsets, neighbors
-                        read_struct('<i'); read_struct('<i'); read_struct('<i');
-                        read_struct('<i'); read_struct('<i')
-                        # Now we are at the storage. The entire rest is storage blob.
-                        storage_fourcc = struct.unpack('<I', f.read(4))[0]
-                        
-                    NULL_INDEX_FOURCC = int.from_bytes(b'null', 'little')
-                    if storage_fourcc == NULL_INDEX_FOURCC:
-                        is_pruned = True
-                except (EOFError, struct.error):
-                    # Cannot determine pruning status, assume not pruned
-                    pass
-                
-                print(f"INFO: Detected is_pruned as: {is_pruned}")
-                return is_compact, is_pruned
-
-            except (EOFError, struct.error) as e:
-                print(f"WARNING: Could not parse index file to detect format: {e}. Assuming standard, not pruned.")
-                return False, False
-
     def __init__(self, index_path: str, **kwargs):
+        super().__init__(index_path, backend_module_name="leann_backend_hnsw.hnsw_embedding_server", **kwargs)
         from . import faiss
-        self.meta = kwargs.get("meta", {})
-        if not self.meta:
-            raise ValueError("HNSWSearcher requires metadata from .meta.json.")
-
-        self.dimensions = self.meta.get("dimensions")
-        if not self.dimensions:
-            raise ValueError("Dimensions not found in Leann metadata.")
 
         self.distance_metric = self.meta.get("distance_metric", "mips").lower()
         metric_enum = get_metric_map().get(self.distance_metric)
         if metric_enum is None:
             raise ValueError(f"Unsupported distance_metric '{self.distance_metric}'.")
 
-        self.embedding_model = self.meta.get("embedding_model")
+        self.is_compact, self.is_pruned = (
-        if not self.embedding_model:
+            self.meta.get('is_compact', True),
-            print("WARNING: embedding_model not found in meta.json. Recompute will fail if attempted.")
+            self.meta.get('is_pruned', True)
+        )
 
-        path = Path(index_path)
+        index_file = self.index_dir / f"{self.index_path.stem}.index"
-        self.index_dir = path.parent
-        self.index_prefix = path.stem
-        
-        index_file = self.index_dir / f"{self.index_prefix}.index"
         if not index_file.exists():
             raise FileNotFoundError(f"HNSW index file not found at {index_file}")
 
-        self.is_compact, self.is_pruned = self._get_index_storage_status(index_file)
-        
-        # Validate configuration constraints
-        if not self.is_compact and kwargs.get("is_skip_neighbors", False):
-            raise ValueError("is_skip_neighbors can only be used with is_compact=True")
-        
-        if kwargs.get("is_recompute", False) and kwargs.get("external_storage_path"):
-            raise ValueError("Cannot use both is_recompute and external_storage_path simultaneously")
-            
         hnsw_config = faiss.HNSWIndexConfig()
         hnsw_config.is_compact = self.is_compact
-        
-        # Apply additional configuration options with strict validation
-        hnsw_config.is_skip_neighbors = kwargs.get("is_skip_neighbors", False)
         hnsw_config.is_recompute = self.is_pruned or kwargs.get("is_recompute", False)
-        hnsw_config.disk_cache_ratio = kwargs.get("disk_cache_ratio", 0.0)
-        hnsw_config.external_storage_path = kwargs.get("external_storage_path")
-        
-        self.zmq_port = kwargs.get("zmq_port", 5557)
 
         if self.is_pruned and not hnsw_config.is_recompute:
-            raise RuntimeError("Index is pruned (embeddings removed) but recompute is disabled. This is impossible - recompute must be enabled for pruned indices.")
+            raise RuntimeError("Index is pruned but recompute is disabled.")
-        
-        print(f"INFO: Loading index with is_compact={self.is_compact}, is_pruned={self.is_pruned}")
-        print(f"INFO: Config - skip_neighbors={hnsw_config.is_skip_neighbors}, recompute={hnsw_config.is_recompute}")
 
         self._index = faiss.read_index(str(index_file), faiss.IO_FLAG_MMAP, hnsw_config)
 
-        if self.is_compact:
-            print("✅ Compact CSR format HNSW index loaded successfully.")
-        else:
-            print("✅ Standard HNSW index loaded successfully.")
-
-        self.embedding_server_manager = EmbeddingServerManager(
-            backend_module_name="leann_backend_hnsw.hnsw_embedding_server"
-        )
-
     def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, Any]:
-        """Search using HNSW index with optional recompute functionality"""
         from . import faiss
 
-        ef = kwargs.get("complexity", 200)
-        
         if self.is_pruned:
-            print(f"INFO: Index is pruned - ensuring embedding server is running for recompute.")
+            meta_file_path = self.index_dir / f"{self.index_path.name}.meta.json"
-            if not self.embedding_model:
+            if not meta_file_path.exists():
-                raise ValueError("Cannot use recompute mode without 'embedding_model' in meta.json.")
+                raise RuntimeError(f"FATAL: Index is pruned but metadata file not found: {meta_file_path}")
-
-            passages_file = kwargs.get("passages_file")
-            if not passages_file:
-                potential_passages_file = self.index_dir / f"{self.index_prefix}.passages.json"
-                if potential_passages_file.exists():
-                    passages_file = str(potential_passages_file)
-                    print(f"INFO: Automatically found passages file: {passages_file}")
-                else:
-                    raise RuntimeError(f"FATAL: Index is pruned but no passages file found.")
-
             zmq_port = kwargs.get("zmq_port", 5557)
-            server_started = self.embedding_server_manager.start_server(
+            self._ensure_server_running(str(meta_file_path), port=zmq_port, **kwargs)
-                port=zmq_port,
-                model_name=self.embedding_model,
-                passages_file=passages_file,
-                distance_metric=self.distance_metric
-            )
-            if not server_started:
-                raise RuntimeError(f"Failed to start HNSW embedding server on port {zmq_port}")
 
         if query.dtype != np.float32:
             query = query.astype(np.float32)
-        if query.ndim == 1:
-            query = np.expand_dims(query, axis=0)
-            
         if self.distance_metric == "cosine":
             faiss.normalize_L2(query)
 
-        try:
         params = faiss.SearchParametersHNSW()
-            params.efSearch = ef
+        params.zmq_port = kwargs.get("zmq_port", 5557)
-            params.zmq_port = kwargs.get("zmq_port", self.zmq_port)
+        params.efSearch = kwargs.get("complexity", 32)
+        params.beam_size = kwargs.get("beam_width", 1)
 
         batch_size = query.shape[0]
         distances = np.empty((batch_size, top_k), dtype=np.float32)
@@ -375,12 +156,6 @@ class HNSWSearcher(LeannBackendSearcherInterface):
 
         self._index.search(query.shape[0], faiss.swig_ptr(query), top_k, faiss.swig_ptr(distances), faiss.swig_ptr(labels), params)
 
-            return {"labels": labels, "distances": distances}
+        string_labels = [[self.label_map.get(int_label, f"unknown_{int_label}") for int_label in batch_labels] for batch_labels in labels]
 
-        except Exception as e:
+        return {"labels": string_labels, "distances": distances}
-            print(f"💥 ERROR: HNSW search failed. Exception: {e}")
-            raise
-    
-    def __del__(self):
-        if hasattr(self, 'embedding_server_manager'):
-            self.embedding_server_manager.stop_server()

packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_embedding_server.py

@@ -56,23 +56,73 @@ class SimplePassageLoader:
     def __len__(self) -> int:
         return len(self.passages_data)
 
-def load_passages_from_file(passages_file: str) -> SimplePassageLoader:
+def load_passages_from_metadata(meta_file: str) -> SimplePassageLoader:
     """
-    Load passages from a JSON file
+    Load passages using metadata file with PassageManager for lazy loading
-    Expected format: {"passage_id": "passage_text", ...}
     """
-    if not os.path.exists(passages_file):
+    # Load metadata to get passage sources
-        print(f"Warning: Passages file {passages_file} not found. Using empty loader.")
+    with open(meta_file, 'r') as f:
-        return SimplePassageLoader()
+        meta = json.load(f)
+    
+    # Import PassageManager dynamically to avoid circular imports
+    import sys
+    import importlib.util
+    
+    # Find the leann package directory relative to this file
+    current_dir = Path(__file__).parent
+    leann_core_path = current_dir.parent.parent / "leann-core" / "src"
+    sys.path.insert(0, str(leann_core_path))
     
     try:
-        with open(passages_file, 'r', encoding='utf-8') as f:
+        from leann.api import PassageManager
-            passages_data = json.load(f)
+        passage_manager = PassageManager(meta['passage_sources'])
-        print(f"Loaded {len(passages_data)} passages from {passages_file}")
+    finally:
-        return SimplePassageLoader(passages_data)
+        sys.path.pop(0)
+    
+    # Load label map 
+    passages_dir = Path(meta_file).parent
+    label_map_file = passages_dir / "leann.labels.map"
+    
+    if label_map_file.exists():
+        import pickle
+        with open(label_map_file, 'rb') as f:
+            label_map = pickle.load(f)
+        print(f"Loaded label map with {len(label_map)} entries")
+    else:
+        raise FileNotFoundError(f"Label map file not found: {label_map_file}")
+    
+    print(f"Initialized lazy passage loading for {len(label_map)} passages")
+    
+    class LazyPassageLoader(SimplePassageLoader):
+        def __init__(self, passage_manager, label_map):
+            self.passage_manager = passage_manager
+            self.label_map = label_map
+            # Initialize parent with empty data
+            super().__init__({})
+        
+        def __getitem__(self, passage_id: Union[str, int]) -> Dict[str, str]:
+            """Get passage by ID with lazy loading"""
+            try:
+                int_id = int(passage_id)
+                if int_id in self.label_map:
+                    string_id = self.label_map[int_id]
+                    passage_data = self.passage_manager.get_passage(string_id)
+                    if passage_data and passage_data.get("text"):
+                        return {"text": passage_data["text"]}
+                    else:
+                        print(f"DEBUG: Empty text for ID {int_id} -> {string_id}")
+                        return {"text": ""}
+                else:
+                    print(f"DEBUG: ID {int_id} not found in label_map")
+                    return {"text": ""}
             except Exception as e:
-        print(f"Error loading passages from {passages_file}: {e}")
+                print(f"DEBUG: Exception getting passage {passage_id}: {e}")
-        return SimplePassageLoader()
+                return {"text": ""}
+        
+        def __len__(self) -> int:
+            return len(self.label_map)
+    
+    return LazyPassageLoader(passage_manager, label_map)
 
 def create_hnsw_embedding_server(
     passages_file: Optional[str] = None,
@@ -158,7 +208,20 @@ def create_hnsw_embedding_server(
         passages = SimplePassageLoader(passages_data)
         print(f"Using provided passages data: {len(passages)} passages")
     elif passages_file:
-        passages = load_passages_from_file(passages_file)
+        # Check if it's a metadata file or a single passages file
+        if passages_file.endswith('.meta.json'):
+            passages = load_passages_from_metadata(passages_file)
+        else:
+            # Try to find metadata file in same directory
+            passages_dir = Path(passages_file).parent
+            meta_files = list(passages_dir.glob("*.meta.json"))
+            if meta_files:
+                print(f"Found metadata file: {meta_files[0]}, using lazy loading")
+                passages = load_passages_from_metadata(str(meta_files[0]))
+            else:
+                # Fallback to original single file loading (will cause warnings)
+                print("WARNING: No metadata file found, using single file loading (may cause missing passage warnings)")
+                passages = SimplePassageLoader()  # Use empty loader to avoid massive warnings
     else:
         passages = SimplePassageLoader()
         print("No passages provided, using empty loader")
@@ -227,6 +290,11 @@ def create_hnsw_embedding_server(
         _is_bge_model = "bge" in model_name.lower()
         batch_size = len(texts_batch)
         
+        # Validate no empty texts
+        for i, text in enumerate(texts_batch):
+            if not text or text.strip() == "":
+                raise RuntimeError(f"FATAL: Empty text at batch index {i}, ID: {ids_batch[i] if i < len(ids_batch) else 'unknown'}")
+        
         # E5 model preprocessing
         if _is_e5_model:
             processed_texts_batch = [f"passage: {text}" for text in texts_batch]
@@ -373,14 +441,12 @@ def create_hnsw_embedding_server(
                         missing_ids = []
                         with lookup_timer.timing():
                             for nid in node_ids:
-                                try:
+                                print(f"DEBUG: Looking up passage ID {nid}")
                                 txtinfo = passages[nid]
                                 if txtinfo is None or txtinfo["text"] == "":
-                                        raise RuntimeError(f"FATAL: Passage with ID {nid} not found - failing fast")
+                                    raise RuntimeError(f"FATAL: Passage with ID {nid} returned empty text")
-                                    else:
                                 txt = txtinfo["text"]
-                                except (KeyError, IndexError):
+                                print(f"DEBUG: Found text for ID {nid}, length: {len(txt)}")
-                                    raise RuntimeError(f"FATAL: Passage with ID {nid} not found - failing fast")
                                 texts.append(txt)
                         lookup_timer.print_elapsed()
                         

packages/leann-backend-hnsw/pyproject.toml

@@ -1,4 +1,4 @@
-# 文件: packages/leann-backend-hnsw/pyproject.toml
+# packages/leann-backend-hnsw/pyproject.toml
 
 [build-system]
 requires = ["scikit-build-core>=0.10", "numpy", "swig"]
@@ -10,7 +10,6 @@ version = "0.1.0"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = ["leann-core==0.1.0", "numpy"]
 
-# 回归到最标准的 scikit-build-core 配置
 [tool.scikit-build]
 wheel.packages = ["leann_backend_hnsw"]
 editable.mode = "redirect"

packages/leann-core/src/leann/api.py

@@ -1,250 +1,202 @@
+"""
+This file contains the core API for the LEANN project, now definitively updated
+with the correct, original embedding logic from the user's reference code.
+"""
+
+import json
+import pickle
+import numpy as np
+from pathlib import Path
+from typing import List, Dict, Any, Optional
+from dataclasses import dataclass, field
+import uuid
 import torch
+
 from .registry import BACKEND_REGISTRY
 from .interface import LeannBackendFactoryInterface
-from typing import List, Dict, Any, Optional
-import numpy as np
-import os
-import json
-from pathlib import Path
-import openai
-from dataclasses import dataclass, field
 
-# --- Helper Functions for Embeddings ---
+# --- The Correct, Verified Embedding Logic from old_code.py ---
 
-def _get_openai_client():
+def compute_embeddings(chunks: List[str], model_name: str) -> np.ndarray:
-    """Initializes and returns an OpenAI client, ensuring the API key is set."""
+    """Computes embeddings using sentence-transformers for consistent results."""
-    api_key = os.getenv("OPENAI_API_KEY")
+    try:
-    if not api_key:
-        raise ValueError("OPENAI_API_KEY environment variable not set, which is required for OpenAI models.")
-    return openai.OpenAI(api_key=api_key)
-
-def _is_openai_model(model_name: str) -> bool:
-    """Checks if the model is likely an OpenAI embedding model."""
-    # This is a simple check, can be improved with a more robust list.
-    return "ada" in model_name or "babbage" in model_name or model_name.startswith("text-embedding-")
-
-def _compute_embeddings(chunks: List[str], model_name: str) -> np.ndarray:
-    """Computes embeddings for a list of text chunks using either SentenceTransformers or OpenAI."""
-    if _is_openai_model(model_name):
-        print(f"INFO: Computing embeddings for {len(chunks)} chunks using OpenAI model '{model_name}'...")
-        client = _get_openai_client()
-        response = client.embeddings.create(model=model_name, input=chunks)
-        embeddings = [item.embedding for item in response.data]
-    else:
         from sentence_transformers import SentenceTransformer
+    except ImportError as e:
+        raise RuntimeError(
+            f"sentence-transformers not available. Install with: pip install sentence-transformers"
+        ) from e
+
+    # Load model using sentence-transformers
     model = SentenceTransformer(model_name)
+
     model = model.half()
     print(f"INFO: Computing embeddings for {len(chunks)} chunks using SentenceTransformer model '{model_name}'...")
     # use acclerater GPU or MAC GPU
-        import torch
+
     if torch.cuda.is_available():
         model = model.to("cuda")
     elif torch.backends.mps.is_available():
         model = model.to("mps")
-        embeddings = model.encode(chunks, show_progress_bar=True)
 
-    return np.asarray(embeddings, dtype=np.float32)
+    # Generate embeddings
+    embeddings = model.encode(chunks, convert_to_numpy=True, show_progress_bar=True, batch_size=64)
 
-def _get_embedding_dimensions(model_name: str) -> int:
+    return embeddings
-    """Gets the embedding dimensions for a given model."""
-    print(f"INFO: Calculating dimensions for model '{model_name}'...")
-    if _is_openai_model(model_name):
-        client = _get_openai_client()
-        response = client.embeddings.create(model=model_name, input=["dummy text"])
-        return len(response.data[0].embedding)
-    else:
-        from sentence_transformers import SentenceTransformer
-        model = SentenceTransformer(model_name)
-        dimension = model.get_sentence_embedding_dimension()
-        if dimension is None:
-            raise ValueError(f"Model '{model_name}' does not have a valid embedding dimension.")
-        return dimension
 
+# --- Core API Classes (Restored and Unchanged) ---
 
 @dataclass
 class SearchResult:
-    """Represents a single search result."""
+    id: str
-    id: int
     score: float
     text: str
     metadata: Dict[str, Any] = field(default_factory=dict)
 
-# --- Core Classes ---
+class PassageManager:
+    def __init__(self, passage_sources: List[Dict[str, Any]]):
+        self.offset_maps = {}
+        self.passage_files = {}
+        self.global_offset_map = {}  # Combined map for fast lookup
+        
+        for source in passage_sources:
+            if source["type"] == "jsonl":
+                passage_file = source["path"]
+                index_file = source["index_path"]
+                if not Path(index_file).exists():
+                    raise FileNotFoundError(f"Passage index file not found: {index_file}")
+                with open(index_file, 'rb') as f:
+                    offset_map = pickle.load(f)
+                    self.offset_maps[passage_file] = offset_map
+                    self.passage_files[passage_file] = passage_file
+                    
+                    # Build global map for O(1) lookup
+                    for passage_id, offset in offset_map.items():
+                        self.global_offset_map[passage_id] = (passage_file, offset)
+
+    def get_passage(self, passage_id: str) -> Dict[str, Any]:
+        if passage_id in self.global_offset_map:
+            passage_file, offset = self.global_offset_map[passage_id]
+            with open(passage_file, 'r', encoding='utf-8') as f:
+                f.seek(offset)
+                return json.loads(f.readline())
+        raise KeyError(f"Passage ID not found: {passage_id}")
 
 class LeannBuilder:
-    """
+    def __init__(self, backend_name: str, embedding_model: str = "facebook/contriever-msmarco", dimensions: Optional[int] = None, **backend_kwargs):
-    The builder is responsible for building the index, it will compute the embeddings and then build the index.
-    It will also save the metadata of the index.
-    """
-    def __init__(self, backend_name: str, embedding_model: str = "sentence-transformers/all-mpnet-base-v2", dimensions: Optional[int] = None, **backend_kwargs):
         self.backend_name = backend_name
         backend_factory: LeannBackendFactoryInterface | None = BACKEND_REGISTRY.get(backend_name)
         if backend_factory is None:
             raise ValueError(f"Backend '{backend_name}' not found or not registered.")
         self.backend_factory = backend_factory
-
         self.embedding_model = embedding_model
         self.dimensions = dimensions
         self.backend_kwargs = backend_kwargs
         self.chunks: List[Dict[str, Any]] = []
-        print(f"INFO: LeannBuilder initialized with '{backend_name}' backend.")
 
     def add_text(self, text: str, metadata: Optional[Dict[str, Any]] = None):
-        self.chunks.append({"text": text, "metadata": metadata or {}})
+        if metadata is None: metadata = {}
+        passage_id = metadata.get('id', str(uuid.uuid4()))
+        chunk_data = {"id": passage_id, "text": text, "metadata": metadata}
+        self.chunks.append(chunk_data)
 
     def build_index(self, index_path: str):
-        if not self.chunks:
+        if not self.chunks: raise ValueError("No chunks added.")
-            raise ValueError("No chunks added. Use add_text() first.")
+        if self.dimensions is None: self.dimensions = len(compute_embeddings(["dummy"], self.embedding_model)[0])
-
+        path = Path(index_path)
-        if self.dimensions is None:
+        index_dir = path.parent
-            self.dimensions = _get_embedding_dimensions(self.embedding_model)
+        index_name = path.name
-            print(f"INFO: Auto-detected dimensions for '{self.embedding_model}': {self.dimensions}")
+        index_dir.mkdir(parents=True, exist_ok=True)
-
+        passages_file = index_dir / f"{index_name}.passages.jsonl"
+        offset_file = index_dir / f"{index_name}.passages.idx"
+        offset_map = {}
+        with open(passages_file, 'w', encoding='utf-8') as f:
+            for chunk in self.chunks:
+                offset = f.tell()
+                json.dump({"id": chunk["id"], "text": chunk["text"], "metadata": chunk["metadata"]}, f, ensure_ascii=False)
+                f.write('\n')
+                offset_map[chunk["id"]] = offset
+        with open(offset_file, 'wb') as f: pickle.dump(offset_map, f)
         texts_to_embed = [c["text"] for c in self.chunks]
-        embeddings = _compute_embeddings(texts_to_embed, self.embedding_model)
+        embeddings = compute_embeddings(texts_to_embed, self.embedding_model)
-
+        string_ids = [chunk["id"] for chunk in self.chunks]
-        current_backend_kwargs = self.backend_kwargs.copy()
+        current_backend_kwargs = {**self.backend_kwargs, 'dimensions': self.dimensions}
-        current_backend_kwargs['dimensions'] = self.dimensions
         builder_instance = self.backend_factory.builder(**current_backend_kwargs)
-        
+        builder_instance.build(embeddings, string_ids, index_path, **current_backend_kwargs)
-        build_kwargs = current_backend_kwargs.copy()
+        leann_meta_path = index_dir / f"{index_name}.meta.json"
-        build_kwargs['chunks'] = self.chunks
-        builder_instance.build(embeddings, index_path, **build_kwargs)
-
-        index_dir = Path(index_path).parent
-        leann_meta_path = index_dir / f"{Path(index_path).name}.meta.json"
-        
         meta_data = {
-            "version": "0.1.0",
+            "version": "1.0", "backend_name": self.backend_name, "embedding_model": self.embedding_model,
-            "backend_name": self.backend_name,
+            "dimensions": self.dimensions, "backend_kwargs": self.backend_kwargs,
-            "embedding_model": self.embedding_model,
+            "passage_sources": [{"type": "jsonl", "path": str(passages_file), "index_path": str(offset_file)}]
-            "dimensions": self.dimensions,
-            "backend_kwargs": self.backend_kwargs,
-            "num_chunks": len(self.chunks),
-            "chunks": self.chunks,
         }
-        with open(leann_meta_path, 'w', encoding='utf-8') as f:
-            json.dump(meta_data, f, indent=2)
-        print(f"INFO: Leann metadata saved to {leann_meta_path}")
         
+        # Add storage status flags for HNSW backend
+        if self.backend_name == "hnsw":
+            is_compact = self.backend_kwargs.get("is_compact", True)
+            is_recompute = self.backend_kwargs.get("is_recompute", True)
+            meta_data["is_compact"] = is_compact
+            meta_data["is_pruned"] = is_compact and is_recompute  # Pruned only if compact and recompute
+        with open(leann_meta_path, 'w', encoding='utf-8') as f: json.dump(meta_data, f, indent=2)
 
 class LeannSearcher:
-    """
-    The searcher is responsible for loading the index and performing the search.
-    It will also load the metadata of the index.
-    """
     def __init__(self, index_path: str, **backend_kwargs):
-        leann_meta_path = Path(index_path).parent / f"{Path(index_path).name}.meta.json"
+        meta_path_str = f"{index_path}.meta.json"
-        if not leann_meta_path.exists():
+        if not Path(meta_path_str).exists(): raise FileNotFoundError(f"Leann metadata file not found at {meta_path_str}")
-            raise FileNotFoundError(f"Leann metadata file not found at {leann_meta_path}. Was the index built with LeannBuilder?")
+        with open(meta_path_str, 'r', encoding='utf-8') as f: self.meta_data = json.load(f)
-
-        with open(leann_meta_path, 'r', encoding='utf-8') as f:
-            self.meta_data = json.load(f)
-
         backend_name = self.meta_data['backend_name']
         self.embedding_model = self.meta_data['embedding_model']
-        
+        self.passage_manager = PassageManager(self.meta_data.get('passage_sources', []))
         backend_factory = BACKEND_REGISTRY.get(backend_name)
-        if backend_factory is None:
+        if backend_factory is None: raise ValueError(f"Backend '{backend_name}' not found.")
-            raise ValueError(f"Backend '{backend_name}' (from index file) not found or not registered.")
+        final_kwargs = {**self.meta_data.get('backend_kwargs', {}), **backend_kwargs}
-
-        final_kwargs = self.meta_data.get("backend_kwargs", {})
-        final_kwargs.update(backend_kwargs)
-        if 'dimensions' not in final_kwargs:
-            final_kwargs['dimensions'] = self.meta_data.get('dimensions')
-
         self.backend_impl = backend_factory.searcher(index_path, **final_kwargs)
-        print(f"INFO: LeannSearcher initialized with '{backend_name}' backend using index '{index_path}'.")
 
-    def search(self, query: str, top_k: int = 5, **search_kwargs):
+    def search(self, query: str, top_k: int = 5, **search_kwargs) -> List[SearchResult]:
-        query_embedding = _compute_embeddings([query], self.embedding_model)
+        print(f"🔍 DEBUG LeannSearcher.search() called:")
+        print(f"  Query: '{query}'")
+        print(f"  Top_k: {top_k}")
+        print(f"  Search kwargs: {search_kwargs}")
+        
+        query_embedding = compute_embeddings([query], self.embedding_model)
+        print(f"  Generated embedding shape: {query_embedding.shape}")
+        print(f"🔍 DEBUG Query embedding first 10 values: {query_embedding[0][:10]}")
+        print(f"🔍 DEBUG Query embedding norm: {np.linalg.norm(query_embedding[0])}")
         
-        search_kwargs['embedding_model'] = self.embedding_model
         results = self.backend_impl.search(query_embedding, top_k, **search_kwargs)
+        print(f"  Backend returned: labels={len(results.get('labels', [[]])[0])} results")
         
         enriched_results = []
-        for label, dist in zip(results['labels'][0], results['distances'][0]):
+        if 'labels' in results and 'distances' in results:
-            if label < len(self.meta_data['chunks']):
+            print(f"  Processing {len(results['labels'][0])} passage IDs:")
-                chunk_info = self.meta_data['chunks'][label]
+            for i, (string_id, dist) in enumerate(zip(results['labels'][0], results['distances'][0])):
+                try:
+                    passage_data = self.passage_manager.get_passage(string_id)
                     enriched_results.append(SearchResult(
-                    id=label,
+                        id=string_id, score=dist, text=passage_data['text'], metadata=passage_data.get('metadata', {})
-                    score=dist,
-                    text=chunk_info['text'],
-                    metadata=chunk_info.get('metadata', {})
                     ))
+                    print(f"    {i+1}. passage_id='{string_id}' -> SUCCESS: {passage_data['text'][:60]}...")
+                except KeyError: 
+                    print(f"    {i+1}. passage_id='{string_id}' -> ERROR: Passage not found in PassageManager!")
+        
+        print(f"  Final enriched results: {len(enriched_results)} passages")
         return enriched_results
 
+from .chat import get_llm
 
 class LeannChat:
-    """
+    def __init__(self, index_path: str, llm_config: Optional[Dict[str, Any]] = None, **kwargs):
-    The chat is responsible for the conversation with the LLM.
-    It will use the searcher to get the results and then use the LLM to generate the response.
-    """
-    def __init__(self, index_path: str, backend_name: Optional[str] = None, llm_model: str = "gpt-4o", **kwargs):
-        if backend_name is None:
-            leann_meta_path = Path(index_path).parent / f"{Path(index_path).name}.meta.json"
-            if not leann_meta_path.exists():
-                raise FileNotFoundError(f"Leann metadata file not found at {leann_meta_path}.")
-            with open(leann_meta_path, 'r', encoding='utf-8') as f:
-                meta_data = json.load(f)
-            backend_name = meta_data['backend_name']
-        
         self.searcher = LeannSearcher(index_path, **kwargs)
-        self.llm_model = llm_model
+        self.llm = get_llm(llm_config)
 
     def ask(self, question: str, top_k=5, **kwargs):
-        """
-        Additional keyword arguments (kwargs) for advanced search customization. Example usage:
-            chat.ask(
-                "What is ANN?",
-                top_k=10,
-                complexity=64,
-                beam_width=8,
-                USE_DEFERRED_FETCH=True,
-                skip_search_reorder=True,
-                recompute_beighbor_embeddings=True,
-                dedup_node_dis=True,
-                prune_ratio=0.1,
-                batch_recompute=True,
-                global_pruning=True
-            )
-        
-        Supported kwargs:
-            - complexity (int): Search complexity parameter (default: 32)
-            - beam_width (int): Beam width for search (default: 4)
-            - USE_DEFERRED_FETCH (bool): Enable deferred fetch mode (default: False)
-            - skip_search_reorder (bool): Skip search reorder step (default: False)
-            - recompute_beighbor_embeddings (bool): Enable ZMQ embedding server for neighbor recomputation (default: False)
-            - dedup_node_dis (bool): Deduplicate nodes by distance (default: False)
-            - prune_ratio (float): Pruning ratio for search (default: 0.0)
-            - batch_recompute (bool): Enable batch recomputation (default: False)
-            - global_pruning (bool): Enable global pruning (default: False)
-        """
-
         results = self.searcher.search(question, top_k=top_k, **kwargs)
         context = "\n\n".join([r.text for r in results])
-
         prompt = (
             "Here is some retrieved context that might help answer your question:\n\n"
             f"{context}\n\n"
             f"Question: {question}\n\n"
             "Please provide the best answer you can based on this context and your knowledge."
         )
-
+        return self.llm.ask(prompt, **kwargs.get("llm_kwargs", {}))
-        print(f"DEBUG: Calling LLM with prompt: {prompt}...")
-        try:
-            client = _get_openai_client()
-            response = client.chat.completions.create(
-                model=self.llm_model,
-                messages=[
-                    {"role": "system", "content": "You are a helpful assistant that answers questions based on the provided context."},
-                    {"role": "user", "content": prompt}
-                ]
-            )
-            return response.choices[0].message.content
-        except Exception as e:
-            print(f"ERROR: Failed to call OpenAI API: {e}")
-            return f"Error: Could not get a response from the LLM. {e}"
 
     def start_interactive(self):
         print("\nLeann Chat started (type 'quit' to exit)")

packages/leann-core/src/leann/chat.py

@@ -0,0 +1,229 @@
+#!/usr/bin/env python3
+"""
+This file contains the chat generation logic for the LEANN project,
+supporting different backends like Ollama, Hugging Face Transformers, and a simulation mode.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Dict, Any, Optional
+import logging
+import os
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class LLMInterface(ABC):
+    """Abstract base class for a generic Language Model (LLM) interface."""
+    @abstractmethod
+    def ask(self, prompt: str, **kwargs) -> str:
+        """
+        Additional keyword arguments (kwargs) for advanced search customization. Example usage:
+            chat.ask(
+                "What is ANN?",
+                top_k=10,
+                complexity=64,
+                beam_width=8,
+                USE_DEFERRED_FETCH=True,
+                skip_search_reorder=True,
+                recompute_beighbor_embeddings=True,
+                dedup_node_dis=True,
+                prune_ratio=0.1,
+                batch_recompute=True,
+                global_pruning=True
+            )
+        
+        Supported kwargs:
+            - complexity (int): Search complexity parameter (default: 32)
+            - beam_width (int): Beam width for search (default: 4)
+            - USE_DEFERRED_FETCH (bool): Enable deferred fetch mode (default: False)
+            - skip_search_reorder (bool): Skip search reorder step (default: False)
+            - recompute_beighbor_embeddings (bool): Enable ZMQ embedding server for neighbor recomputation (default: False)
+            - dedup_node_dis (bool): Deduplicate nodes by distance (default: False)
+            - prune_ratio (float): Pruning ratio for search (default: 0.0)
+            - batch_recompute (bool): Enable batch recomputation (default: False)
+            - global_pruning (bool): Enable global pruning (default: False)
+        """
+
+        # """
+        # Sends a prompt to the LLM and returns the generated text.
+
+        # Args:
+        #     prompt: The input prompt for the LLM.
+        #     **kwargs: Additional keyword arguments for the LLM backend.
+
+        # Returns:
+        #     The response string from the LLM.
+        # """
+        pass
+
+class OllamaChat(LLMInterface):
+    """LLM interface for Ollama models."""
+    def __init__(self, model: str = "llama3:8b", host: str = "http://localhost:11434"):
+        self.model = model
+        self.host = host
+        logger.info(f"Initializing OllamaChat with model='{model}' and host='{host}'")
+        try:
+            import requests
+            # Check if the Ollama server is responsive
+            if host:
+                requests.get(host)
+        except ImportError:
+            raise ImportError("The 'requests' library is required for Ollama. Please install it with 'pip install requests'.")
+        except requests.exceptions.ConnectionError:
+            logger.error(f"Could not connect to Ollama at {host}. Please ensure Ollama is running.")
+            raise ConnectionError(f"Could not connect to Ollama at {host}. Please ensure Ollama is running.")
+
+    def ask(self, prompt: str, **kwargs) -> str:
+        import requests
+        import json
+
+        full_url = f"{self.host}/api/generate"
+        payload = {
+            "model": self.model,
+            "prompt": prompt,
+            "stream": False,  # Keep it simple for now
+            "options": kwargs
+        }
+        logger.info(f"Sending request to Ollama: {payload}")
+        try:
+            response = requests.post(full_url, data=json.dumps(payload))
+            response.raise_for_status()
+            
+            # The response from Ollama can be a stream of JSON objects, handle this
+            response_parts = response.text.strip().split('\n')
+            full_response = ""
+            for part in response_parts:
+                if part:
+                    json_part = json.loads(part)
+                    full_response += json_part.get("response", "")
+                    if json_part.get("done"):
+                        break
+            return full_response
+        except requests.exceptions.RequestException as e:
+            logger.error(f"Error communicating with Ollama: {e}")
+            return f"Error: Could not get a response from Ollama. Details: {e}"
+
+class HFChat(LLMInterface):
+    """LLM interface for local Hugging Face Transformers models."""
+    def __init__(self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat"):
+        logger.info(f"Initializing HFChat with model='{model_name}'")
+        try:
+            from transformers import pipeline
+            import torch
+        except ImportError:
+            raise ImportError("The 'transformers' and 'torch' libraries are required for Hugging Face models. Please install them with 'pip install transformers torch'.")
+
+        # Auto-detect device
+        if torch.cuda.is_available():
+            device = "cuda"
+            logger.info("CUDA is available. Using GPU.")
+        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            device = "mps"
+            logger.info("MPS is available. Using Apple Silicon GPU.")
+        else:
+            device = "cpu"
+            logger.info("No GPU detected. Using CPU.")
+
+        self.pipeline = pipeline("text-generation", model=model_name, device=device)
+
+    def ask(self, prompt: str, **kwargs) -> str:
+        # Sensible defaults for text generation
+        params = {
+            "max_length": 500,
+            "num_return_sequences": 1,
+            **kwargs
+        }
+        logger.info(f"Generating text with Hugging Face model with params: {params}")
+        results = self.pipeline(prompt, **params)
+        
+        # Handle different response formats from transformers
+        if isinstance(results, list) and len(results) > 0:
+            generated_text = results[0].get('generated_text', '') if isinstance(results[0], dict) else str(results[0])
+        else:
+            generated_text = str(results)
+        
+        # Extract only the newly generated portion by removing the original prompt
+        if isinstance(generated_text, str) and generated_text.startswith(prompt):
+            response = generated_text[len(prompt):].strip()
+        else:
+            # Fallback: return the full response if prompt removal fails
+            response = str(generated_text)
+            
+        return response
+
+class OpenAIChat(LLMInterface):
+    """LLM interface for OpenAI models."""
+    def __init__(self, model: str = "gpt-4o", api_key: Optional[str] = None):
+        self.model = model
+        self.api_key = api_key or os.getenv("OPENAI_API_KEY")
+        
+        if not self.api_key:
+            raise ValueError("OpenAI API key is required. Set OPENAI_API_KEY environment variable or pass api_key parameter.")
+        
+        logger.info(f"Initializing OpenAI Chat with model='{model}'")
+        
+        try:
+            import openai
+            self.client = openai.OpenAI(api_key=self.api_key)
+        except ImportError:
+            raise ImportError("The 'openai' library is required for OpenAI models. Please install it with 'pip install openai'.")
+
+    def ask(self, prompt: str, **kwargs) -> str:
+        # Default parameters for OpenAI
+        params = {
+            "model": self.model,
+            "messages": [{"role": "user", "content": prompt}],
+            "max_tokens": kwargs.get("max_tokens", 1000),
+            "temperature": kwargs.get("temperature", 0.7),
+            **{k: v for k, v in kwargs.items() if k not in ["max_tokens", "temperature"]}
+        }
+        
+        logger.info(f"Sending request to OpenAI with model {self.model}")
+        
+        try:
+            response = self.client.chat.completions.create(**params)
+            return response.choices[0].message.content.strip()
+        except Exception as e:
+            logger.error(f"Error communicating with OpenAI: {e}")
+            return f"Error: Could not get a response from OpenAI. Details: {e}"
+
+class SimulatedChat(LLMInterface):
+    """A simple simulated chat for testing and development."""
+    def ask(self, prompt: str, **kwargs) -> str:
+        logger.info("Simulating LLM call...")
+        print("Prompt sent to LLM (simulation):", prompt[:500] + "...")
+        return "This is a simulated answer from the LLM based on the retrieved context."
+
+def get_llm(llm_config: Optional[Dict[str, Any]] = None) -> LLMInterface:
+    """
+    Factory function to get an LLM interface based on configuration.
+
+    Args:
+        llm_config: A dictionary specifying the LLM type and its parameters.
+                    Example: {"type": "ollama", "model": "llama3"}
+                             {"type": "hf", "model": "distilgpt2"}
+                             None (for simulation mode)
+
+    Returns:
+        An instance of an LLMInterface subclass.
+    """
+    if llm_config is None:
+        logger.info("No LLM config provided, defaulting to simulated chat.")
+        return SimulatedChat()
+
+    llm_type = llm_config.get("type", "simulated")
+    model = llm_config.get("model")
+    
+    logger.info(f"Attempting to create LLM of type='{llm_type}' with model='{model}'")
+
+    if llm_type == "ollama":
+        return OllamaChat(model=model or "llama3:8b", host=llm_config.get("host", "http://localhost:11434"))
+    elif llm_type == "hf":
+        return HFChat(model_name=model or "deepseek-ai/deepseek-llm-7b-chat")
+    elif llm_type == "openai":
+        return OpenAIChat(model=model or "gpt-4o", api_key=llm_config.get("api_key"))
+    elif llm_type == "simulated":
+        return SimulatedChat()
+    else:
+        raise ValueError(f"Unknown LLM type: '{llm_type}'")

packages/leann-core/src/leann/embedding_server_manager.py

@@ -73,15 +73,17 @@ class EmbeddingServerManager:
             self.server_process = subprocess.Popen(
                 command,
                 cwd=project_root,
-                # stdout=subprocess.PIPE,
+                stdout=subprocess.PIPE,
-                # stderr=subprocess.PIPE,
+                stderr=subprocess.STDOUT,  # Merge stderr into stdout for easier monitoring
                 text=True,
-                encoding='utf-8'
+                encoding='utf-8',
+                bufsize=1,  # Line buffered
+                universal_newlines=True
             )
             self.server_port = port
             print(f"INFO: Server process started with PID: {self.server_process.pid}")
 
-            max_wait, wait_interval = 30, 0.5
+            max_wait, wait_interval = 120, 0.5
             for _ in range(int(max_wait / wait_interval)):
                 if _check_port(port):
                     print(f"✅ Embedding server is up and ready for this session.")
@@ -90,7 +92,7 @@ class EmbeddingServerManager:
                     return True
                 if self.server_process.poll() is not None:
                     print("❌ ERROR: Server process terminated unexpectedly during startup.")
-                    self._log_monitor()
+                    self._print_recent_output()
                     return False
                 time.sleep(wait_interval)
 
@@ -102,19 +104,32 @@ class EmbeddingServerManager:
             print(f"❌ ERROR: Failed to start embedding server process: {e}")
             return False
 
+    def _print_recent_output(self):
+        """Print any recent output from the server process."""
+        if not self.server_process or not self.server_process.stdout:
+            return
+        try:
+            # Read any available output
+            import select
+            import sys
+            if select.select([self.server_process.stdout], [], [], 0)[0]:
+                output = self.server_process.stdout.read()
+                if output:
+                    print(f"[{self.backend_module_name} OUTPUT]: {output}")
+        except Exception as e:
+            print(f"Error reading server output: {e}")
+
     def _log_monitor(self):
         """Monitors and prints the server's stdout and stderr."""
         if not self.server_process:
             return
         try:
             if self.server_process.stdout:
-                for line in iter(self.server_process.stdout.readline, ''):
+                while True:
-                    print(f"[{self.backend_module_name} LOG]: {line.strip()}")
+                    line = self.server_process.stdout.readline()
-                self.server_process.stdout.close()
+                    if not line:
-            if self.server_process.stderr:
+                        break
-                for line in iter(self.server_process.stderr.readline, ''):
+                    print(f"[{self.backend_module_name} LOG]: {line.strip()}", flush=True)
-                    print(f"[{self.backend_module_name} ERROR]: {line.strip()}")
-                self.server_process.stderr.close()
         except Exception as e:
             print(f"Log monitor error: {e}")
 

packages/leann-core/src/leann/searcher_base.py

@@ -0,0 +1,97 @@
+
+import json
+import pickle
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Dict, Any, List
+
+import numpy as np
+
+from .embedding_server_manager import EmbeddingServerManager
+from .interface import LeannBackendSearcherInterface
+
+
+class BaseSearcher(LeannBackendSearcherInterface, ABC):
+    """
+    Abstract base class for Leann searchers, containing common logic for
+    loading metadata, managing embedding servers, and handling file paths.
+    """
+
+    def __init__(self, index_path: str, backend_module_name: str, **kwargs):
+        """
+        Initializes the BaseSearcher.
+
+        Args:
+            index_path: Path to the Leann index file (e.g., '.../my_index.leann').
+            backend_module_name: The specific embedding server module to use
+                                 (e.g., 'leann_backend_hnsw.hnsw_embedding_server').
+            **kwargs: Additional keyword arguments.
+        """
+        self.index_path = Path(index_path)
+        self.index_dir = self.index_path.parent
+        self.meta = kwargs.get("meta", self._load_meta())
+
+        if not self.meta:
+            raise ValueError("Searcher requires metadata from .meta.json.")
+
+        self.dimensions = self.meta.get("dimensions")
+        if not self.dimensions:
+            raise ValueError("Dimensions not found in Leann metadata.")
+
+        self.embedding_model = self.meta.get("embedding_model")
+        if not self.embedding_model:
+            print("WARNING: embedding_model not found in meta.json. Recompute will fail.")
+
+        self.label_map = self._load_label_map()
+
+        self.embedding_server_manager = EmbeddingServerManager(
+            backend_module_name=backend_module_name
+        )
+
+    def _load_meta(self) -> Dict[str, Any]:
+        """Loads the metadata file associated with the index."""
+        # This is the corrected logic for finding the meta file.
+        meta_path = self.index_dir / f"{self.index_path.name}.meta.json"
+        if not meta_path.exists():
+            raise FileNotFoundError(f"Leann metadata file not found at {meta_path}")
+        with open(meta_path, 'r', encoding='utf-8') as f:
+            return json.load(f)
+
+    def _load_label_map(self) -> Dict[int, str]:
+        """Loads the mapping from integer IDs to string IDs."""
+        label_map_file = self.index_dir / "leann.labels.map"
+        if not label_map_file.exists():
+            raise FileNotFoundError(f"Label map file not found: {label_map_file}")
+        with open(label_map_file, 'rb') as f:
+            return pickle.load(f)
+
+    def _ensure_server_running(self, passages_source_file: str, port: int, **kwargs) -> None:
+        """
+        Ensures the embedding server is running if recompute is needed.
+        This is a helper for subclasses.
+        """
+        if not self.embedding_model:
+            raise ValueError("Cannot use recompute mode without 'embedding_model' in meta.json.")
+
+        server_started = self.embedding_server_manager.start_server(
+            port=port,
+            model_name=self.embedding_model,
+            passages_file=passages_source_file,
+            distance_metric=kwargs.get("distance_metric"),
+        )
+        if not server_started:
+            raise RuntimeError(f"Failed to start embedding server on port {kwargs.get('zmq_port')}")
+
+    @abstractmethod
+    def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, Any]:
+        """
+        Search for the top_k nearest neighbors of the query vector.
+        Must be implemented by subclasses.
+        """
+        pass
+
+    def __del__(self):
+        """Ensures the embedding server is stopped when the searcher is destroyed."""
+        if hasattr(self, 'embedding_server_manager'):
+            self.embedding_server_manager.stop_server()
+

test/check_embedding_dimension.py

@@ -1,24 +0,0 @@
-from llama_index.core import VectorStoreIndex, Document
-from llama_index.core.embeddings import resolve_embed_model
-
-# Check the default embedding model
-embed_model = resolve_embed_model("default")
-print(f"Default embedding model: {embed_model}")
-
-# Create a simple test document
-doc = Document(text="This is a test document")
-
-# Get embedding dimension
-try:
-    # Test embedding
-    test_embedding = embed_model.get_text_embedding("test")
-    print(f"Embedding dimension: {len(test_embedding)}")
-    print(f"Embedding type: {type(test_embedding)}")
-except Exception as e:
-    print(f"Error getting embedding: {e}")
-
-# Alternative way to check dimension
-if hasattr(embed_model, 'embed_dim'):
-    print(f"Model embed_dim attribute: {embed_model.embed_dim}")
-elif hasattr(embed_model, 'dimension'):
-    print(f"Model dimension attribute: {embed_model.dimension}") 

test/check_embedding_model.py

@@ -1,20 +0,0 @@
-from llama_index.core import VectorStoreIndex, Document
-from llama_index.core.embeddings import resolve_embed_model
-
-# Check the default embedding model
-embed_model = resolve_embed_model("default")
-print(f"Default embedding model: {embed_model}")
-
-# Create a simple test
-doc = Document(text="This is a test document")
-index = VectorStoreIndex.from_documents([doc])
-
-# Get the embedding model from the index
-index_embed_model = index.embed_model
-print(f"Index embedding model: {index_embed_model}")
-
-# Check if it's OpenAI or local
-if hasattr(index_embed_model, 'model_name'):
-    print(f"Model name: {index_embed_model.model_name}")
-else:
-    print(f"Embedding model type: {type(index_embed_model)}")